Non-convex approaches for low-rank tensor completion under tubal sampling

Zheng Tan, Longxiu Huang, HanQin Cai, Yifei Lou
3/17/2023
cs.LGcs.CVmath.NA

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Abstract

Tensor completion is an important problem in modern data analysis. In this work, we investigate a specific sampling strategy, referred to as tubal sampling. We propose two novel non-convex tensor completion frameworks that are easy to implement, named tensor $L_1$-$L_2$ (TL12) and tensor completion via CUR (TCCUR). We test the efficiency of both methods on synthetic data and a color image inpainting problem. Empirical results reveal a trade-off between the accuracy and time efficiency of these two methods in a low sampling ratio. Each of them outperforms some classical completion methods in at least one aspect.

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Cite this paper

@article{tan2023nonconvex,
  title  = {Non-convex approaches for low-rank tensor completion under tubal sampling},
  author = {Zheng Tan and Longxiu Huang and HanQin Cai and Yifei Lou},
  year   = {2023},
  eprint = {2303.12721},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.12721v1}
}

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